nep-ppm New Economics Papers
on Project, Program and Portfolio Management
Issue of 2013‒07‒28
six papers chosen by
Arvi Kuura
Parnu College - Tartu University

  1. The Long and Winding Road: Valuing Investment under Construction Uncertainty By Jacco J.J. Thijssen
  2. Endogenous Matching in University-Industry Collaboration: Theory and Empirical Evidence from the UK By Albert Banal-Estañol; Inés Macho-Stadler; David Pérez-Castrillo
  3. Business Intelligence Support For Project Management By Muntean, Mihaela; Cabau, Liviu Gabiel
  4. Evaluating A Business Intelligence Solution. Feasibility Analysis Based On Monte Carlo Method By Muntean, Mihaela; Muntean, Cornelia
  5. New empirical findings for international investment in intangible assets By Martin Falk
  6. Quantifying the Impact of Leveraging and Diversification on Systemic Risk By Tasca, Paolo; Mavrodiev, Pavlin; Schweitzer, Frank

  1. By: Jacco J.J. Thijssen
    Abstract: This paper presents a model of investment in projects that are characterized by (i) uncertainty over both the construction costs and revenues, and (ii) revenues that accrue only after construction is completed. Both processes are modeled as spectrally negative Levy jump-diffusions. The optimal stopping problem that determines the value of the project is solved under fairly general assumptions. It is found that the threshold for the benefit-to-cost ratio (BCR) beyond which investment is optimal is higher than when investment costs are sunk and upfront. In addition, the current value of the BCR decreases sharply in the frequency of negative shocks to the construction process. This implies that the cost overruns that can be expected if one ignores such shocks are sharply increasing in their frequency. Based on calibrated data, the model is applied to the construction of high-speed rail in the UK and it is found that the economic case for the first phase of High Speed 2 cannot be made and is unlikely to be met in the next 10 years.
    Keywords: Investment under Uncertainty, Infrastructure investment, Optimal stopping
    JEL: G31 C61
    Date: 2013–07
  2. By: Albert Banal-Estañol; Inés Macho-Stadler; David Pérez-Castrillo
    Abstract: We develop a two-sided matching model to analyze collaboration between heterogeneous academics and ï¬rms. We predict a positive assortative matching in terms of both scientiï¬c ability and affinity for type of research, but negative assortative in terms of ability on one side and affinity in the other. In addition, the most able and most applied academics and the most able and most basic ï¬rms shall collaborate rather than stay independent. Our predictions receive strong support from the analysis of the teams of academics and ï¬rms that propose research projects to the UK’s Engineering and Physical Sciences Research Council.
    Keywords: matching, industry-science links, research collaborations, basic versus applied research, complementarity
    JEL: O32 I23
    Date: 2013–07
  3. By: Muntean, Mihaela; Cabau, Liviu Gabiel
    Abstract: With respect to the project management framework, a project live cycle consists of phases like: initiation, planning, execution, monitoring & control and closing. Monitoring implies measuring the progress and performance of the project during its execution and communicating the status. Actual performance is compared with the planned one. Therefore, a minimal set of key performance indicators will be proposed. Monitoring the schedule progress, the project budget and the scope will be possible. Within a Business Intelligence initiative, monitoring is possible by attaching the key performance indicators to the OLAP cube. In turn, the cube was deployed over a proper data warehouse schema.
    Keywords: project management, business intelligence, key performance indicators
    JEL: L21 M0
    Date: 2013–03–08
  4. By: Muntean, Mihaela; Muntean, Cornelia
    Abstract: Business Intelligence (BI) initiatives are challenging tasks, implying significant costs in their implementation. Therefore, organizations have adopted prudent policies requiring a financial justification. A business-driven methodology is recommended in any BI project initiative, project scoping and planning being vital for the project success. A business-driven approach of a BI project implementation starts with a feasibility study. The decision-making process for large projects is very complicated, and will not be subject of this paper. Having in mind a middle-sized BI project, a feasibility study based on the Monte Carlo simulation method will be conducted. A SaaS BI initiative versus a traditional one will be taken into consideration.
    Keywords: Business Intelligence (BI), Software as a Service (SaaS), Monte Carlo method, BI project feasibility, Total Cost of Ownership (TCO), Return on Investment (ROI), Internal Rate of Return (IRR)
    JEL: C02 C88 G17 L21 L86 M15
    Date: 2012–11–18
  5. By: Martin Falk
    Abstract: This study empirically analyses the determinants of greenfield investment in intangible assets in emerging and industrialized countries. Data consists of host parent country pairs of greenfield FDI projects in (i) software (except video games), (ii) advertising, public relations and related activities, (iii) headquarters, (iv) research & development and (v) design, development & testing. With a world market share of 33 per cent in 2011 in terms of the number of projects, descriptive statistics show that the EU 27 is one of the most important locations for international greenfield investment in intangible assets. However, there was a decline in the EU 27s share of such projects after the recent financial and economic crisis, which is mainly due to the decrease in intra-EU greenfield FDI activities. In contrast, FDI inflows in intangible assets increased in the United States, in other non EU OECD countries and in emerging countries. Among the EU countries of Ireland, Luxembourg, the United Kingdom, Denmark, Belgium, Netherlands and Sweden are the most attractive locations for Non-EU investors, whereas the southern and East EU countries are least successful in attracting FDI projects in intangible assets. The results using fixed and random effects negative binomial regression models for 40 host and 26 parent countries during the period 2003–2010 show that FDI in intangible assets depends significantly positively on quantity of human capital, quality of human capital measured as the PISA score in maths and reading, costs of starting a business, broadband penetration, strength of investor protection, R&D endowment and direct R&D subsidies. Wage costs (or unit labour costs) have a significant negative impact on FDI inflows in intangible assets. Other policy factors, such as labour market regulations, product, or FDI regulations, do not have a significant impact. Separate estimates for the EU-27 countries show that corporate taxes matter for the international location decision for intangible assets. The empirical results presented may help to develop a proactive action plan to attract international investments in intangible assets in Europe.
    Keywords: Innovation, innovation policy, intangible assets
    JEL: O3
    Date: 2013–07
  6. By: Tasca, Paolo; Mavrodiev, Pavlin; Schweitzer, Frank
    Abstract: Excessive leverage, i.e. the abuse of debt financing, is considered one of the  primary factors in the default of financial institutions. Systemic risk results from correlations between individual default probabilities that cannot be considered independent. Based on the structural framework by Merton (1974), we discuss a model in which these  correlations arise from overlaps in banks' portfolios. Portfolio  diversification is used as a strategy to mitigate losses from investments in risky projects. We calculate an optimal level of  diversification that has to be reached for a given level of excessive leverage to still mitigate an increase in systemic risk. In our  model, this optimal diversification further depends on the market size and the market conditions (e.g. volatility). It allows to distinguish between a safe regime, in which excessive leverage does not result in an increase of systemic risk, and a risky regime, in which excessive leverage cannot be mitigated leading to an increased systemic risk. Our results are of relevance for financial regulators.
    Keywords: Business, Systemic Risk, Leverage, Diversification
    Date: 2013–03–22

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